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Forecasting the Vote: A Theoretical Comparison of Election Markets and Public Opinion Polls

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  • Kou, S. G.
  • Sobel, Michael E.

Abstract

The dominant methodology for short-term forecasting of electoral outcomes uses trial-heat polls, where respondents report their current electoral preferences (not their election-day predictions). Election markets, where self-selected participants trade shares of candidates at prices predictive of election-day results, provide an alternative method that often produces more accurate forecasts. Consequently, increasing attention is being paid to this methodology. However, it is poorly understood and lacks theoretical justification. Surprisingly, the rationale for forecasting using trial-heat polls has not been completely developed either. We develop the justification for using both election markets and public opinion polls to forecast electoral outcomes, giving conditions under which each method performs ideally. For the ideal case, we prove (under the reasonable assumption that market participants are aware of the poll results) that the mean square prediction error for the market forecast is smaller than that of any forecast based on one or more polls. The case in which the assumptions supporting each method fail is also considered. It is often reasonable to expect that the best case results hold approximately, in which case the market forecast should also beat any poll-based forecast. We also compare the bias and variance of market and poll-based forecasts; our results suggest the utility of using the series of market prices to study the course of campaigns.

Suggested Citation

  • Kou, S. G. & Sobel, Michael E., 2004. "Forecasting the Vote: A Theoretical Comparison of Election Markets and Public Opinion Polls," Political Analysis, Cambridge University Press, vol. 12(3), pages 277-295, July.
  • Handle: RePEc:cup:polals:v:12:y:2004:i:03:p:277-295_00
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    Cited by:

    1. Quinlan, Stephen & Lewis-Beck, Michael S., 2021. "Forecasting government support in Irish general elections: Opinion polls and structural models," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1654-1665.
    2. Bunker, Kenneth, 2020. "A two-stage model to forecast elections in new democracies," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1407-1419.
    3. Coulomb, Renaud & Sangnier, Marc, 2014. "The impact of political majorities on firm value: Do electoral promises or friendship connections matter?," Journal of Public Economics, Elsevier, vol. 115(C), pages 158-170.
    4. Dai, Min & Jia, Yanwei & Kou, Steven, 2021. "The wisdom of the crowd and prediction markets," Journal of Econometrics, Elsevier, vol. 222(1), pages 561-578.
    5. Renaud Coulomb & Marc Sangnier, 2014. "The Impact of Political Majorities on Firm Value: Do Electoral Promises or Friendship Connections Matter?," PSE Working Papers halshs-00990241, HAL.
    6. Ahmed, Rashad & Pesaran, M. Hashem, 2022. "Regional heterogeneity and U.S. presidential elections: Real-time 2020 forecasts and evaluation," International Journal of Forecasting, Elsevier, vol. 38(2), pages 662-687.
    7. Robert Reig & Ramona Schoder, 2010. "Forecasting Accuracy: Comparing Prediction Markets And Surveys – An Experimental Study," Journal of Prediction Markets, University of Buckingham Press, vol. 4(3), pages 1-19.
    8. Khan, Urmee & Lieli, Robert P., 2018. "Information flow between prediction markets, polls and media: Evidence from the 2008 presidential primaries," International Journal of Forecasting, Elsevier, vol. 34(4), pages 696-710.
    9. Wiesen, Taylor, 2023. "Aggregate earnings and market expectations in United States presidential election prediction markets," Advances in accounting, Elsevier, vol. 60(C).
    10. Reade, J. James & Vaughan Williams, Leighton, 2019. "Polls to probabilities: Comparing prediction markets and opinion polls," International Journal of Forecasting, Elsevier, vol. 35(1), pages 336-350.
    11. Coulomb, Renaud & Sangnier, Marc, 2014. "The impact of political majorities on firm value: Do electoral promises or friendship connections matter?," Journal of Public Economics, Elsevier, vol. 115(C), pages 158-170.
    12. John Fry & Andrew Brint, 2017. "Bubbles, Blind-Spots and Brexit," Risks, MDPI, vol. 5(3), pages 1-15, July.
    13. Davis, Brent, 2015. "Forecasting Elections: Do Prediction Markets Tells Us Anything More than the Polls?," MPRA Paper 65505, University Library of Munich, Germany.

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